Paper Citation Record · LEDGER

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction

As of 21 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2605.02230.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2605.02230 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-01T00:55:42.705580Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T22:32:52.434354Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact3
verified fuzzy28
unresolved1
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 578e34e2-e09e-4e38-8d2d-a7087d5a237a · outbound

This paper cites The 2021 who classification of tumors of the central nervous system: a summary.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction The 2021 who classification of tumors of the central nervous system: a summary

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.846411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 32fdd7cc-22dc-43f2-b882-946de5bb3159 · outbound

This paper cites The multimodal brain tumor image segmentation benchmark (brats).

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction The multimodal brain tumor image segmentation benchmark (brats)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.832913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fab2fd22-e1ae-4c57-846d-fce24d6dd7dc · outbound

This paper cites Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-01T13:15:45.467379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a21176dc-a178-4f44-a5fd-9d1b1264da04 · outbound

This paper cites Role of surgical resection in low-and high-grade gliomas.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Role of surgical resection in low-and high-grade gliomas

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.842222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 019b3592-ec77-47c1-ac26-032325e99d09 · outbound

This paper cites Diffuse glioma growth: a guerilla war.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Diffuse glioma growth: a guerilla war

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.844323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1323971d-3589-4d5f-89ef-aa675f1c505a · outbound

This paper cites Cost of mi- gration: invasion of malignant gliomas and implications for treatment.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Cost of mi- gration: invasion of malignant gliomas and implications for treatment

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.852662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 801fc31c-8997-4ecd-8e9a-09b295454168 · outbound

This paper cites 3d u-net: learning dense volumetric segmentation from sparse annotation.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction 3d u-net: learning dense volumetric segmentation from sparse annotation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.855150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7385b8fb-3142-419a-ad1c-e895ee3a2ba6 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.850555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d4d72846-a063-40ff-8829-e91e7d209228 · outbound

This paper cites Unetr: Trans- formers for 3d medical image segmentation.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Unetr: Trans- formers for 3d medical image segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.837683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8cb01215-0943-4296-b15c-e799e206beea · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.835410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3850a2ec-0ffa-4d87-89c7-a9cccf429199 · outbound

This paper cites 3d mri brain tumor segmentation using autoen- coder regularization.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction 3d mri brain tumor segmentation using autoen- coder regularization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.863582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 56ba492c-caf5-425b-a030-a3443b36cd73 · outbound

This paper cites Deep learning based brain tumor segmentation: a survey.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Deep learning based brain tumor segmentation: a survey

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.848533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0a88a219-b001-4d2e-aceb-f3aacb3e4f58 · outbound

This paper cites A quantitative model for differential motility of gliomas in grey and white matter.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction A quantitative model for differential motility of gliomas in grey and white matter

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.830935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 40cbbe44-4392-478b-861d-b8d025ff1039 · outbound

This paper cites Radiomic mri signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond idh1.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Radiomic mri signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond idh1

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.840069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c5fd8905-564a-4356-970d-d00c92e8b84a · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction U-net: Convolutional networks for biomedical image segmentation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.861137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 38ed56d3-9eb8-430c-8aa1-ac607c53da9b · outbound

This paper cites Two-stage cascaded u- net: 1st place solution to brats challenge 2019 segmentation task.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Two-stage cascaded u- net: 1st place solution to brats challenge 2019 segmentation task

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.840868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 78890792-8213-4871-8c6b-d759464e2ba8 · outbound

This paper cites Brain tumor segmentation using deep learning techniques on multi-institutional MRI datasets.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Brain tumor segmentation using deep learning techniques on multi-institutional MRI datasets

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.843028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 279f7029-1304-457f-aaca-dd9c18b9bdba · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction nnu-net: a self-configuring method for deep learning-based biomedical image segmentation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.845263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 80aeedae-1d8b-4316-82a8-c8a864d298ee · outbound

This paper cites an unresolved cited work.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-07-07T07:53:27.847205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3cac3eb3-4eef-4154-9c33-a4bf9bdbe018 · outbound

This paper cites Quality-aware bag of modulation spectrum features for robust speech emotion recognition.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Quality-aware bag of modulation spectrum features for robust speech emotion recognition

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.851707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f0110b6b-dca5-4e55-8b8d-dcf4f9c0c1ec · outbound

This paper cites Task-specific speech enhancement and data augmentation for improved multimodal emotion recognition under noisy conditions.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Task-specific speech enhancement and data augmentation for improved multimodal emotion recognition under noisy conditions

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.827861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c5219ac7-1f80-4979-8d9d-18340ceaa8b6 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-01T13:15:45.459535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 47dec593-12ef-4658-83e2-547e8f3c2bea · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Swin transformer: Hierarchical vision transformer using shifted windows

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.825671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b96be4e7-8d86-486b-bd42-499be4bf2a1f · outbound

This paper cites Transbts: Multimodal brain tumor segmentation using transformer.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Transbts: Multimodal brain tumor segmentation using transformer

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.829897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5e2168c2-3587-4b48-be69-64ff68cb2936 · outbound

This paper cites Self-supervised pre- training of swin transformers for 3d medical image analysis.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Self-supervised pre- training of swin transformers for 3d medical image analysis

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.834129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eef5a168-1395-49a2-9904-061816fb03ac · outbound

This paper cites Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.836410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e3acceab-30a6-4738-9f74-c6a2a1c71529 · outbound

This paper cites An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.832126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 35c6d5e4-01d0-44e4-8c6f-45ddd6b03ae1 · outbound

This paper cites Imaging sur- rogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Imaging sur- rogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.838526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0f9caede-ce6a-4688-8782-b36d7b97dee1 · outbound

This paper cites Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.823490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ada29bab-7115-4145-8c2a-7b229a63b640 · outbound

This paper cites Visualizing and understanding con- volutional networks.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Visualizing and understanding con- volutional networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.818749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e4f6542c-373c-47a6-9956-6985c59a1d81 · outbound

This paper cites Explainable deep learning models in medical image analysis.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Explainable deep learning models in medical image analysis

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T07:53:27.821221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 181c16e0-caab-447d-b0dc-e741825df6ed · outbound

This paper cites Decoupled Weight Decay Regularization.

InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction Decoupled Weight Decay Regularization

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-07-01T13:15:45.463887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 9a416ddb-cf92-4e48-a2dd-2e2c562dd6a6 · inbound

CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data cites this paper.

CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T22:32:52.434354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.